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Impact of data smoothing on semantic segmentation

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Abstract

Semantic segmentation is the process to classify each pixel of an image. The current state-of-the-art semantic segmentation techniques use end-to-end trainable deep models. Generally, the training of these models is controlled by some external hyper-parameters rather to use the variation in data. In this paper, we investigate the impact of data smoothing on the training and generalization of deep semantic segmentation models. A mechanism is proposed to select the best level of smoothing to get better generalization of the deep semantic segmentation models. Furthermore, a smoothing layer is included in the deep semantic segmentation models to automatically adjust the level of smoothing. Extensive experiments are performed to validate the effectiveness of the proposed smoothing strategies.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad-Abbottabad Campus, University Road, Abbottabad, Pakistan

    Nuhman Ul Haq, Zia ur Rehman, Ahmad Khan, Ahmad Din & Sajid Shah

  2. School of Mathematical Computer Sciences, Heriot-Watt University Dubai Campus Dubai International Academic City, Dubai, UAE

    Abrar Ullah

  3. Department of Computer Science, IT University of Malakand, Chakdara, KPK, Pakistan

    Fawad Qayum

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  1. Nuhman Ul Haq
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  2. Zia ur Rehman
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  3. Ahmad Khan
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Correspondence to Ahmad Khan.

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Ul Haq, N., ur Rehman, Z., Khan, A. et al. Impact of data smoothing on semantic segmentation. Neural Comput & Applic 34, 8345–8354 (2022). https://doi.org/10.1007/s00521-020-05341-4

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